Artificial Intelligence, Personalised Persuasion, and Climate Attitudes

Elena Pro, António Valentim

European Institute, London School of Economics

Climate Information and Climate Concern

  • The Problem: Persuading people to care about and act toward climate change is still one of the major collective challenges we face as a society
  • Effective communication is crucial
    • Over $15 million spent in science-based public information campaigns (United Nations 2025)
  • However, effects are small and short-lived (Deryugina and Shurchkov 2016; van der Linden 2017).

In Practice:

Climate change image

80%
care about climate change

BUT
don’t do anything about it

Why is this happening?

  • The issue: Traditional information campaigns often fail because they don’t engage people personally
    • Psychological distance and intertemporal discounting reduce climate urgency (Trope & Liberman, 2010).
    • Emotional disconnection from abstract risks (Weber, 2006).

The Problem

People see why climate matters generally, but not why it matters to them

The solution:

  • Tailored messaging aligned with individual beliefs and experiences is more effective
  • But traditional targeting faces critical limitations:
    • Operates at group level (demographics, ideology)
    • Expensive
    • ⟹ Hard to scale

AI as a Solution

Key Advantages:

  • From a practical perspective, AI can:
    • Scale: Reach millions with personalised messages
    • Cost-effective: Reduce costs significantly
    • Engage: Create tailored conversations to the individual level


  • From a theoretical perspective, AI can:
    • Evidence-based reasoning (Costello et al., 2025)
    • Non-judgmental interaction (Goel et al., 2024)
    • Responsive dialogue (Santoro et al., 2024)

Hypotheses

H1: When compared with the control condition (information provision), interacting with the LLM will have a positive effect on all outcomes of interest: a) climate concern, b) policy support, c) pro-environmental behavior, d) conversation spillover, e) political spillover, and f) subjective climate beliefs.


H2: The personalized LLM interaction on climate change will be more persuasive/effective than the non-personalized LLM interaction and the basic information provision on all outcomes of interest.


H3: The personalized LLM interaction on climate change condition will be more persuasive/effective compared to the personalized AI interaction on an unrelated political topic.

Research Design

Online Survey Experiment:

  • Sample
    • N = 1,500-2,000 UK adults (representative sample)
    • Recruited via established survey platform (Prolific)
    • Power analysis: 930 minimum for small-to-medium effect (d = 0.26-0.37)
    • Context: UK offers valuable setting where climate skepticism is growing but not deeply entrenched

The treatments

Participants are randomized into these conditions:

  1. Control: Standard information provision about climate change

  2. Non-Personalized AI: Generic LLM conversation about climate change

  3. Personalized AI (Unrelated Topic): Personalised conversation about a political topic unrelated to climate change

  4. Personalized AI (Climate): Personalised climate conversation about climate change:

Survey Flow

AI diagram

An Example

AI diagram

What this looks like?

AI
Climate Conversation Bot
AI: Hi! I see you care about the environment. What matters most to you - your children's future, energy costs, or something else?
You: Honestly, I worry about my kids
AI: I understand that parental concern. Let me share how climate action today protects the world your children will inherit...
You: Tell me more

Outcome Variables

  • Climate concern: 5-point scale from “Not at all concerned” to “Extremely concerned”
  • Pro-environmental behavior: Willingness to donate portion of $100 prize to climate NGO
  • Conversation spillover: Likelihood of discussing AI conversation with others
  • Political spillover: Choice to write message to Member of Parliament about climate change
  • Subjective climate beliefs: Open-ended question about personal beliefs on climate change + LLM-generated Likert scale

Design Advantage

Pre-post measurement enables both within-subject change detection and between-group comparisons

Field Experiment Integration

Social Media

GP
Greenpeace UK
💬 Curious about climate change?
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Newsletter

Friends of the Earth Weekly
• UK announces new renewable energy targets
• Local community wins against fracking proposal
• New study shows impact of diet on carbon footprint
🤖 Try Our Climate AI

The Numbers

£500
Traditional targeting
per 1,000 people
£35
Our AI approach
per 1,000 people

Thank you!

Any Questions?

📧 e.pro@lse.ac.uk     🐦 @elenapro0     🌐 www.elenapro.eu